Adversarial machine learning: A new frontier in cyber attacks
1 Department of Business Administration, Eastern University, Dhaka, Bangladesh.
2 Department of Electrical and Electronics Engineering, Independent university Bangladesh.
3 Department of Computer Science & Engineering, Daffodil International University Dhaka Bangladesh.
4 Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology (RUET), Bangladesh.
5 MBA in economics, Comilla University.
Research Article
World Journal of Advanced Research and Reviews, 2022, 16(02), 1258-1268
Article DOI: 10.30574/wjarr.2022.16.2.1115
Publication history:
Received on 26 September 2022; revised on 16 November 2022; accepted on 28 November 2022
Abstract:
AML represents an emerging critical issue in cybersecurity that creates severe difficulties for security systems that use AI. AI-based security systems face increasing threats because threat responders use adversarial techniques to exploit vulnerabilities in these systems, as organizations depend on AI more often for threat detection and response. This analysis studies how AML poses an escalating threat to contemporary cyberattacks while affecting the operation of AI security models. A comprehensive analysis of real-world security cases alongside present defense methods allows this paper to reveal the principal flaws that affect AI system security performance. The paper presents recommendations to strengthen AI model resistance against adversarial attacks and suggests potential research directions within this field.
Keywords:
Adversarial Machine; Cybersecurity Systems; Security Breaches; Malicious Use; AI Defenses; Ethical Concerns
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